Google · Agentic AI · Google DeepMind
Despite decades of progress in cheminformatics, chemists still spend too much time wrangling data
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The same molecule can appear as an IUPAC name in a paper, a Markush structure in a patent, or a structure-data file (SDF) in a catalog.
Key facts
- To further advance the pace of research, they're offering 10,000 scientists the highest usage tier in Google Antigravity free of charge for 6 months (onboarded on a rolling basis), subject
- Zoi Tsangalidou, Ruijie (Kelly) Zhang, Sukhdeep Singh and Michael V
- Here they show how Google Antigravity, equipped with their new Cheminformatics Skills, accelerates drug discovery workflows
- The team would like to acknowledge the contributions of the following team members: Filip Radlinski, Artem Sobolev, Victoria Johnston, Hannah Kiely-Collins, Zachary Wu, Jue Wang, Jack Dunger, Adriana
Summary
Zoi Tsangalidou, Ruijie (Kelly) Zhang, Sukhdeep Singh and Michael V. Designing new medicines requires curating complex data from charts, figures and literature, training predictive models, and proposing candidate molecules to synthesize, a workflow that typically takes multiple days for a single iteration. Here they show how Google Antigravity, equipped with their new Cheminformatics Skills, accelerates drug discovery workflows. Chemistry is one of science’s richest public records, spanning millions of structures across journal articles and vendor catalogs. Despite decades of progress in cheminformatics, chemists still spend too much time wrangling data, a process complicated by the fact that molecules can be represented in many ways: from text descriptions to images or data structures.